Reinforcement Learning for Automated Financial Trading: Basics and Applications

Abstract

The construction of automated financial trading systems (FTSs) is a subject of high interest for both the academic environment and the financial one due to the potential promises by self-learning methodologies. In this paper we consider Reinforcement Learning (RL) type algorithms, that is algorithms that real-time optimize their behavior in relation to the responses they get from the environment in which they operate, without the need for a supervisor. In particular, first we introduce the essential aspects of RL which are of interest for our purposes, second we present some original automatic FTSs based on differently configured RL-based algorithms, then we apply such FTSs to artificial and real time series of daily stock prices. Finally, we compare our FTSs with a classical one based on Technical Analysis indicators. All the results we achieve are generally quite satisfactory.